We have previously developed a three-node RNN that predicts customer personalities from an input vector of their classified financial transactions [1]}. This input vector consists of six annual time steps, each consisting of 97 transaction classes; the values in each time step add up to one and are the fraction of a customer's annual spending per transaction category. The RNN output is a five-dimensional personality vector; its values are the degrees of membership in each of five personality traits: openness, conscientiousness, extraversion, agreeableness, and neuroticism. We use the feature trajectories from this model's state space—shown in Figure REF —to represent a customer's spending behavior over time.
<FIGURE>